---
title: "DashBoard"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    theme: default
    source_code: embed
---

```{r setup, include=FALSE}
library(ggplot2)
library(flexdashboard)
library(dplyr)
library(plotly)
df <- read.table("Baseball2010.txt", header = T)
```


Comparing Salary
==========

Row
------------------------------------------
### Avg Team Salary for the American League
```{r}
valueBox(round(mean(df$Salary[df$League == 1]), 2), color = "pink")
```
### Average Team Salary (Mil$)
```{r}
gauge(
  round(mean(df$Salary),
    digits = 2
  ),
  min = 0,
  max = max(df$Salary)
)
```

### Avg Team Salary for the National League
```{r}
valueBox(round(mean(df$Salary[df$League == 0]), 2), color = "dodgerblue")
```
Row
------------------------------------------
### Team Salary for American League
```{r}
p <- df %>%
  filter(League == 1) %>%
  ggplot(aes(x = reorder(Team, Salary, decreasing = T), y = Salary)) +
  geom_bar(stat = "identity", fill = "pink", alpha = 0.8) +
  labs(
    title = "Team Salary Comparison for the American League",
    x = "Team",
    y = "Total 2010 Team Salary ($Mil)"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  coord_cartesian(ylim = c(0, max(df$Salary)))
ggplotly(p)
```

### Team Salary for the National League
```{r}
p <- df %>%
  filter(League == 0) %>%
  ggplot(aes(x = reorder(Team, Salary, decreasing = T), y = Salary)) +
  geom_bar(stat = "identity", fill = "dodgerblue", alpha = 0.5) +
  labs(
    title = "Team Salary Comparison for the National League",
    x = "Team",
    y = "Total 2010 Team Salary ($Mil)"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  coord_cartesian(ylim = c(0, max(df$Salary)))
ggplotly(p)
```

# Win Rate

Row
------------------------------------------
### Relationship between Wins and Batting Average
```{r}
p <- ggplot(df, aes(x = Wins, y = Batting)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(
    title = "Relationship between Wins and Batting Average",
    x = "Number of Wins",
    y = "Batting Average"
  )
ggplotly(p)
```
### Relationship between Wins and HR
```{r}
p <- ggplot(df, aes(x = Wins, y = HR)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(
    title = "Relationship between Wins and HR",
    x = "Number of Wins",
    y = "Batting Average"
  )
ggplotly(p)
```

### Relationship between Wins and Errors
```{r}
p <- ggplot(df, aes(x = Wins, y = Errors)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(
    title = "Relationship between Wins and Errors",
    x = "Number of Wins",
    y = "Batting Average"
  )
ggplotly(p)
```